alphacep / alphacep/vosk-api

I build kannada language from scratch using kaldi with 60hr , how to finetune with 1hr?

Ouverte
#2,050 6 commentaires 0 réactions 0 personnes assignées Voir sur GitHub
Langage dominant
Jupyter Notebook
Étoiles
15.1k
Forks
1.8k
Métriques de merge des PR
Aucune PR mergée en 30 j

Description

I build kannada model from scratch with kaldi all **{ lang.sh, n-gram for language_model.apra , lexicon with own g2p , mfcc , mono, tri 1,2,3 , ivector , chain nnet3 tdnn, dynamic graph with opengram }** - it works good

but how i can fine tune with 1hr of data = i want to make model more powerfull to make branches and add misssing words which improve accuracy

I cant find perfect steps to follow? - **where to find fine tune steps???**

what i got is = **{ extract mfcc of 1hr , by using tri3 of 60hr align_fmllr_lats.sh send that and do ivector - }** like its bit confusing onto their is no proper documentaction is their

please provide steps to finetune.

Guide de contribution

Aucun guide de contribution indexé pour ce dépôt

Évaluation

Cette issue n'a pas encore été évaluée.

Recevez les nouvelles issues par e-mail

Un résumé court des issues GitHub adaptées aux débutants.